ݺߣshows by User: andreparaense / http://www.slideshare.net/images/logo.gif ݺߣshows by User: andreparaense / Wed, 24 Feb 2016 12:35:32 GMT ݺߣShare feed for ݺߣshows by User: andreparaense A machine consciousness approach to urban traffic signal control /slideshow/a-machine-consciousness-approach-to-urban-traffic-signal-control-58656756/58656756 slides-160224123532
In this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the ``Machine Consciousness'' traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 10% to more than 20%. Reference Paraense, A. L. O., Raizer, K. and Gudwin, R.R. (2016). A machine consciousness approach to urban traffic control, Biologically Inspired Cognitive Architectures, Volume 15, January 2016, Pages 61-73, ISSN 2212-683X, http://dx.doi.org/10.1016/j.bica.2015.10.001]]>
In this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the ``Machine Consciousness'' traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 10% to more than 20%. Reference Paraense, A. L. O., Raizer, K. and Gudwin, R.R. (2016). A machine consciousness approach to urban traffic control, Biologically Inspired Cognitive Architectures, Volume 15, January 2016, Pages 61-73, ISSN 2212-683X, http://dx.doi.org/10.1016/j.bica.2015.10.001]]> Wed, 24 Feb 2016 12:35:32 GMT /slideshow/a-machine-consciousness-approach-to-urban-traffic-signal-control-58656756/58656756 andreparaense@slideshare.net(andreparaense) A machine consciousness approach to urban traffic signal control andreparaense In this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the ``Machine Consciousness'' traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 10% to more than 20%. Reference Paraense, A. L. O., Raizer, K. and Gudwin, R.R. (2016). A machine consciousness approach to urban traffic control, Biologically Inspired Cognitive Architectures, Volume 15, January 2016, Pages 61-73, ISSN 2212-683X, http://dx.doi.org/10.1016/j.bica.2015.10.001 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-160224123532-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this work, we present a distributed cognitive architecture used to control the traffic in an urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the ``Machine Consciousness&#39;&#39; traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 10% to more than 20%. Reference Paraense, A. L. O., Raizer, K. and Gudwin, R.R. (2016). A machine consciousness approach to urban traffic control, Biologically Inspired Cognitive Architectures, Volume 15, January 2016, Pages 61-73, ISSN 2212-683X, http://dx.doi.org/10.1016/j.bica.2015.10.001
A machine consciousness approach to urban traffic signal control from Andr辿 Paraense
]]> 500 5 https://cdn.slidesharecdn.com/ss_thumbnails/slides-160224123532-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A machine consciousness approach to urban traffic signal control /slideshow/a-machine-consciousness-approach-to-urban-traffic-signal-control/54856186 slides-151107162509-lva1-app6892
In this work, we present a distributed cognitive architecture used to control the traffic in a urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the “Artificial Consciousness” traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 13.8% to more than 21%.]]>
In this work, we present a distributed cognitive architecture used to control the traffic in a urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the “Artificial Consciousness” traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 13.8% to more than 21%.]]> Sat, 07 Nov 2015 16:25:09 GMT /slideshow/a-machine-consciousness-approach-to-urban-traffic-signal-control/54856186 andreparaense@slideshare.net(andreparaense) A machine consciousness approach to urban traffic signal control andreparaense In this work, we present a distributed cognitive architecture used to control the traffic in a urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the “Artificial Consciousness” traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 13.8% to more than 21%. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-151107162509-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this work, we present a distributed cognitive architecture used to control the traffic in a urban network. This architecture relies on a machine consciousness approach - Global Workspace Theory - in order to use competition and broadcast, allowing a group of local traffic controllers to interact, resulting in a better group performance. The main idea is that the local controllers usually perform a purely reactive behavior, defining the times of red and green lights, according just to local information. These local controllers compete in order to define which of them is experiencing the most critical traffic situation. The controller in the worst condition gains access to the global workspace, further broadcasting its condition (and its location) to all other controllers, asking for their help in dealing with its situation. This call from the controller accessing the global workspace will cause an interference in the reactive local behavior, for those local controllers with some chance in helping the controller in a critical condition, by containing traffic in its direction. This group behavior, coordinated by the global workspace strategy, turns the once reactive behavior into a kind of deliberative one. We show that this strategy is capable of improving the overall mean travel time of vehicles flowing through the urban network. A consistent gain in performance with the “Artificial Consciousness” traffic signal controller during all simulation time, throughout different simulated scenarios, could be observed, ranging from around 13.8% to more than 21%.
A machine consciousness approach to urban traffic signal control from Andr辿 Paraense
]]> 463 4 https://cdn.slidesharecdn.com/ss_thumbnails/slides-151107162509-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-andreparaense-48x48.jpg?cb=1528728604 https://cdn.slidesharecdn.com/ss_thumbnails/slides-160224123532-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-machine-consciousness-approach-to-urban-traffic-signal-control-58656756/58656756 A machine consciousnes... https://cdn.slidesharecdn.com/ss_thumbnails/slides-151107162509-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-machine-consciousness-approach-to-urban-traffic-signal-control/54856186 A machine consciousnes...